Biology and related fields are currently witnessing a revolution. Quantitative approaches and methods are now becoming increasingly common, largely due to remarkable achievements in sequencing technologies and high-throughput genomic screening methods. One of the major challenges is to use such approaches for understanding the underlying mechanisms and functions of genes. The approach that will be taken in this project is to model the genetic regulatory system and infer the model structure and parameters from gene expression data. The goal of the project is to further develop and refine the mathematical and computational methods and models and to apply them to a concrete biological model system. This will not only serve as a validation of the modeling approach, but will also lead to an improved understanding of the genetic regulatory mechanisms underlying cellular function and dysfunction. In particular, the specific aims are 1) to compare different network inference algorithms and validate them with experimental methods;2) to generate and evaluate sub-networks using time-course microarray data;3) to predict and validate the effects of gene perturbations on steady-state expressions of genes in the sub-networks. The research program will closely integrate both computational and experimental approaches to achieve our goals.